Understanding the relationship between the complexity of human learning and associated brain function is one of the most fascinafing journeys of basic science. In addition to being an important academic question, studies of brain function assocIated with learning have very practical applications for improving diagnosis and therapy of learning disabilities. Learning disability affects between 10-20 percent of Americans with severe socioeconomic consequences on their quality of life and health. This proposal focuses on understanding the neural processes underlying normal human learning of auditory information that is transient and occurs in rapid succession. The most intuitive example of such processing is reflected in our ability to learn and understand speech. Deficits in learning such forms of information are associated with dyslexia and language-learning impairment. A few of the currently popular tools used to study the relationships between human learning and associated brain processes are Positron Emission Tomography (PET), Functional Magnetic Resonance Imaging (fMRI), Magnetoencephalography (MEG) and Electroencephalography (EEG). However, of all these methods only MEG and EEG offer adequate time resolution, essential for the proposed study because brain responses to auditory stimuli typically occur in the time-scale of milliseconds. Data obtained using MEG and EEG is often analyzed without consideration of the dynamics of cortical activity and often simplified source and head models are assumed, Information about brain plasticity obtained in this fashion is hard to understand and interpret. Recently several new methods have been developed to process MEG and EEG data. However, the usefulness of these methods has not been adequately demonstrated on real data. The first specific aim of this proposal is to research and to validate novel analyses methods that will enhance the interpretation of EEG and MEG data. We will use realistic head modeling for imaging distributed sources and account for the spatio-temporal dynamics of brain activity. We will empirically validate the usefulness of these methods to understand the dynamics of functional brain plasticity using computer simulations and experiments. The second specific aim of the proposal is to determine the relationship between the dynamics of functional brain plasticity in spatio-temporal responses to successive stimuli and changes in psychophysical thresholds that occur as a result of perceptual learning. We will focus on learning in rate discrimination of amplitude-modulated tone trains in normal adults as a first step towards understanding learning of simple time-varying auditory stimuli that occur in rapid succession. We will examine and correlate learning-induced behavioral changes with changes in the spatial and the temporal patterns of activity within and across cortical areas. Such a multidisciplinary approach which combines methods of scientific computing and functional brain imaging using MEG and EEG should enhance our understanding of general neural mechanisms underlying human perception learning. These results in normal individuals should provide crucial information for the development, refinement and evaluation of diagnosis and therapy for individuals with learning disability.